Uncertainty-Aware Anatomical Brain Parcellation using Diffusion MRI

Motivation: While anatomical brain parcellation has long been performed using anatomical MRI and atlas-based approaches, deep learning methods together with diffusion MRI techniques can improve parcellation performance and interpretation of prediction uncertainty. Goal(s): Our goal is to design an uncertainty-aware deep learning network to utilize multiple diffusion MRI parameters for accurate brain parcellation while enabling voxel-level uncertainty estimation. Approach: We include five evidential deep learning subnetworks and perform an evidence-based ensemble for parcellation prediction and uncertainty estimation. Results: The results demonstrate our method’s superior parcellation performance over several state-of-the-art methods, its promising results in unseen patient scans, and potential applications in brain abnormality detection. Impact: The proposed approach enables improved accuracy in brain parcellation from diffusion MRI, facilitating the understanding of the human brain in health and disease. It may also serve as an effective tool for brain abnormality detection, fostering inquiries into uncertainty-quantified diagnostics.